Search Results for author:
Found 9 papers, 4 papers with code
The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
We analyze the effect of alignment tuning by examining the token distribution shift between base LLMs and their aligned counterpart.
FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions
Theory of mind (ToM) evaluations currently focus on testing models using passive narratives that inherently lack interactivity.
Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting
In this work, we focus on LLM sensitivity to a quintessential class of meaning-preserving design choices: prompt formatting.
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
The ability to derive underlying principles from a handful of observations and then generalize to novel situations -- known as inductive reasoning -- is central to human intelligence.
Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker
We present SymbolicToM, a plug-and-play approach to reason about the belief states of multiple characters in reading comprehension tasks via explicit symbolic representation.
Faith and Fate: Limits of Transformers on Compositionality
We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures.
Referee: Reference-Free Sentence Summarization with Sharper Controllability through Symbolic Knowledge Distillation
Moreover, we uniquely propose iterative distillation of knowledge, where student models from the previous iteration of distillation serve as teacher models in the next iteration.
Symmetric Machine Theory of Mind
Theory of mind (ToM), the ability to understand others' thoughts and desires, is a cornerstone of human intelligence.
Modeling human visual search: A combined Bayesian searcher and saliency map approach for eye movement guidance in natural scenes
Saliency models have been useful to predict fixation locations in natural images, but they provide no information about the time-sequence of fixations.
